Καλησπέρα σας,
Αυριο θα παρουσιάσω το paper με τίτλο
NG-DBSCAN: Scalable Density-Based Clustering for Arbitrary Data
των:
Alessandro Lulli , Matteo Dell’Amico , Pietro Michiardi , Laura Ricci
το οποίο δημοσιεύτηκε στο vldb του 2016. Το abstract του paper:
We present NG-DBSCAN, an approximate density-based clustering algorithm that operates on arbitrary data and any symmetric distance measure. The distributed design of our algorithm makes it scalable to very large datasets; its approximate nature makes it fast, yet capable of producing high quality clustering results. We provide a detailed overview of the steps of NG-DBSCAN, together with their analysis. Our results, obtained through an extensive experimental campaign with real and synthetic data, substantiate our claims about NG-DBSCAN’s performance and scalability.
Nikos Provatas
PhD Candidate
Computing Systems Laboratory (CSLab)
National Technical University of Athens
